Demonstrates that learning systems can stably converge to incorrect solutions when feedback reliability is unobservable.
March 24, 2026
Original Paper
Learning Can Converge Stably to the Wrong Belief under Latent Reliability
arXiv · 2603.21491
The Takeaway
It challenges the assumption that minimizing loss or maximizing reward always leads to the true objective, proving that 'stable' optimization can hide persistent bias. The proposed MTR framework provides a way to infer reliability from the learning dynamics themselves.
From the abstract
Learning systems are typically optimized by minimizing loss or maximizing reward, assuming that improvements in these signals reflect progress toward the true objective. However, when feedback reliability is unobservable, this assumption can fail, and learning algorithms may converge stably to incorrect solutions.This failure arises because single-step feedback does not reveal whether an experience is informative or persistently biased. When information is aggregated over learning trajectories,